We consolidate Nerfies [1]} and HyperNeRF [2]} in one codebase using JAX [3]}.
Compared to the original official code releases, our implementation aligns all training and evaluation details between models and allows correspondence readout.
Our implementation reproduces the quantitative results in the original papers.
We implement T-NeRF in the same codebase.
For NSFF [4]}, we tried both the official code base [5]} and a public third-party re-implementation [6]}, where the former fails to converge on our proposed iPhone dataset while the latter works well.
We thus report results using the third-party re-implementation.
However, note that both the original and the third-party implementation represent the dynamic scene in normalized device coordinates (NDC).
As NDC is designed for forward-facing but not considered inward-facing scenes, layered artifacts may appear due to its log-scale sampling rate in the world space, as shown in Figure REF .
More details about aligning the training procedure and remaining differences are provided in the Appendix.
Code, pretrained models, and data are available on the project page.
